Neural network based learning engine to adapt therapies

ABSTRACT

A system for implementing a cardiac device having adaptive treatment therapies utilizing a neural network based learning engine is disclosed. The system includes an implantable cardiac device module and an external data processing system for specifying the operating characteristics of the cardiac device module. Both the cardiac device module and the external processing system possess an artificial neural network to specify the operation of the cardiac device module as it provides adaptive treatment therapies. The external data processing system includes a complete neural network module that trains and validates the operation of the neural network to match the optimal treatment options with a received set of collected patient data. A runtime neural network module that provides real time operation of the neural network using collected patient data is located within the cardiac device module. The cardiac device module and the external processing module are connected via a communication link.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/668,428, filed on Sep. 23, 2003, now issued as U.S. Pat. No.7,200,435, the specification of which is incorporated herein byreference.

TECHNICAL FIELD

This application relates in general to a method, apparatus, and articleof manufacture for providing adaptive medical therapies utilizing aneural network based learning engine, and more particularly to a method,apparatus, and article of manufacture for implementing an implantablecardiac device having adaptive treatment therapies utilizing a neuralnetwork based learning engine.

BACKGROUND

Implantable cardiac device have recently become increasingly commonplacein providing cardiac therapies to patients in need of constantmonitoring of heart conditions that require immediate treatment. Thesecardiac devices typically provide a single therapy, or at most a fewdifferent therapies, to a patient depending upon a small set ofobservable parameters regarding the condition of a patient's heart. As aresult of the more widespread use of such devices, it is becoming moreevident that the operation of these devices need to be customized toprovide a more optimal set of therapies to any given patient.

The computational capabilities inherent within implantable devices hasincreased along with the general increase in computational technologyduring this same time period. However, performing more complexcomputations also results in increased power consumption on any givencomputational platform, including the devices within implantablesystems. As a result, many data processing strategies have not beenreadily utilized in these devices as the computational requirements ofsuch systems has typically been too complex to be realisticallyutilized.

SUMMARY

This application relates in general to a method, apparatus, and articleof manufacture for providing adaptive medical therapies utilizing aneural network based learning engine. One possible embodiment of thepresent invention is a system providing adaptive medical therapiesutilizing a neural network based learning engine to a cardiac patient.The system includes a cardiac devices module for providing adaptivemedical therapies to the patient, an artificial neural networkprocessing module for training and validating the operation of a neuralnetwork, and a communications link between the cardiac device module andthe artificial neural network processing module. The cardiac devicemodule includes a cardiac devices data collection module for collectingpatient data associated with the cardiac health state of the patient'sheart, a cardiac therapy module for applying corrective medicaltherapies to the patient's heart upon detection of undesired healthconditions, and a runtime neural network module for processing collectedpatient data to determine the corrective medical therapies to be appliedusing the cardiac therapy module. The artificial neural networkprocessing module includes a cardiac neural network training module forprocessing collected patient data to determine a set of operatingcoefficients used by the artificial neural network when determiningoptimal treatment therapies, a cardiac device interface module forreceiving collected patient data from the cardiac device module and fortransmitting the set of operating coefficients associated used by theartificial neural network when determining optimal treatment therapiesand a collected patient data history data store for maintaining all ofthe patient collected data history and treatment therapies. The cardiacdevice runtime neural network module and the neural network trainingmodule implement identical networks of nodes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example embodiment of a cardiac device systemhaving adaptive treatment therapies utilizing a neural network basedlearning engine according to one possible embodiment of the presentinvention.

FIG. 2 illustrates an example embodiment of a cardiac device module thatis part of an overall system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to onepossible embodiment of the present invention.

FIG. 3A illustrates an example embodiment of an artificial neuralnetwork server module for use with a cardiac device module that is partof an overall system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to onepossible embodiment of the present invention.

FIG. 3B illustrates a cardiac device processing system according to anexample embodiment of the present invention.

FIG. 4 illustrates a computing system that may be used to constructvarious computing systems that may be part of a distributed processingand communications system according to one embodiment of the presentinvention.

FIG. 5 illustrates another example embodiment of a cardiac device moduleshowing data flow through a system for providing adaptive treatmenttherapies utilizing a neural network based learning engine according toone possible embodiment of the present invention.

FIG. 6 illustrates another example embodiment of an artificial neuralnetwork server module for use with a cardiac device module showing dataflow through a system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to onepossible embodiment of the present invention.

DETAILED DESCRIPTION

This application relates in general to a method, apparatus, and articleof manufacture for providing adaptive medical therapies utilizing aneural network based learning engine. In the following detaileddescription of exemplary embodiments of the invention, reference is madeto the accompanied drawings, which form a part hereof, and which isshown by way of illustration, specific exemplary embodiments of whichthe invention may be practiced. These embodiments are described insufficient detail to enable those skilled in the art to practice theinvention, and it is to be understood that other embodiments may beutilized, and other changes may be made, without departing from thespirit or scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined only by the appended claims.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The term “connected” means a direct connectionbetween the items connected, without any intermediate devices. The term“coupled” means either a direct connection between the items connected,or an indirect connection through one or more passive or activeintermediary devices. The term “circuit” means either a single componentor a multiplicity of components, either active and/or passive, that arecoupled together to provide a desired function. The term “signal” meansat least one current, voltage, or data signal. Referring to thedrawings, like numbers indicate like parts throughout the views.

FIG. 1 illustrates an example embodiment of a cardiac device systemhaving adaptive treatment therapies utilizing a neural network basedlearning engine according to one possible embodiment of the presentinvention. The cardiac device system includes a cardiac device 200electronically connected to a patient's heart 100 using electricalconnections 101 to obtain data associated with a collection ofobservable parameters related to the health of the heart 100 as well asprovide a collection of medical therapies as needed to maintain ahealthy operation for the heart. The cardiac device 200 contains a setof data operating parameters associated with providing these collectionof medical therapies that are used to control the operation of thecardiac device when it provides these collection of medical therapies tothe heart 100 based upon the collection of observable parametersmeasured by the cardiac device 200.

The data operating parameters associated with providing the collectionof medical therapies may be adjusted periodically to provide a patientwith a more optimal set of therapies for a given set of observedparameters for the operation of the patient's heart 100. Types oftherapies may include, but is not limited to: pacing at physiologicalrates to correct a heartbeat that is too slow (pacemaker therapy: PM),pacing at physiological rates to correct for dissynchrony between thecontractions of the left and right ventricles (heart failure therapy orcardiac resynchronization therapy: HF/CRT), pacing at high rates tobreak cycles of tachycardia (anti-tachycardia pacing: ATP), and shocktherapy to break tachycardia, particular fibrillation (defibrillationshock or shock). For PM and HF/CRT, the cardiac device typicallyprovides a continuous series of pulses, low voltage and narrow in time,just strong enough to stimulate the heart into contraction at controlledtimes or intervals. For ATP, the cardiac device typically provides aburst or set of bursts of narrow pulses at higher voltages. For shock,the cardiac device typically provides a high-voltage pulse ofcharacteristic width and shape. In the case of cardiac devices havingATP and/or shock therapy available, the devices are typically designedto respond with appropriate therapy when tachycardia or fibrillation isdetected. The therapies may be applied to the atria or the ventricles ofthe heart, or both, as indicated by the underlying condition of theheart.

One skilled in the art will recognize that many therapy parameters aretypically provided for adjusting corrective therapies to best treat thecondition of individual patients. Examples may include but not belimited to, the lower rate limit (LRL), atrio-ventricular (AV) delay,maximum tracking rate (MTR), pulse width, and pulse voltage for PM andHF/CRT; the burst cycle length (BCL), number of pulses per burst, numberof burst per attempt, change in BCL per burst, and pulse width andvoltage for ATP; and energy, polarity, waveform (monophasic orbiphasic), and duty cycle (percentage of discharge time spent in onepolarity of a biphasic waveform) for shock. The values of the applicabletherapy parameters, in conjunction with the patient's observedparameters, are provided to an ANN processing system. Values stored incardiac device 200 are provided to the ANN processing system 300 overcommunications link 400. Observed parameters for the patient may beassociated with past therapy attempts, both those existing before thetherapy and those existing in response to this therapy, or may includegeneral trending information in the patient's history, not directlyassociated with a particular therapy attempt. The ANN processing system300 then determines an optimized set of applicable therapy parametersand communicates them to the cardiac device 200 over communications link400.

The communications link 400 may consist of any electrical communicationslink between the processing devices within the cardiac device 200 andthe ANN processing system 300. One skilled in the art will recognizethat such a link may be constructed using an RF communications link, anIR communications link, an electrical communications link, an audiobased communications link, or any other communications link thatprovides digital communications between these devices without deviatingfrom the spirit and scope of the present invention as recited within theattached claims.

FIG. 2 illustrates an example embodiment of a cardiac device module thatis part of an overall system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to onepossible embodiment of the present invention. The cardiac device module200 includes a set of processing modules to perform the operationsassociated with collecting data, providing therapies and communicatingwith the ANN processing system 300. These processing modules include acardiac data interface module 201, a cardiac therapy module 202, acardiac device data collection module 203, a cardiac neural networkruntime module 204, an ANN processing system interface module 205 and alocal data storage module 210.

The cardiac data interface module 201 interfaces the cardiac devicemodule 200 to the patient's heart 100 to collect data regarding thecurrent health of the heart. This data may include but is not limited toa collection of observable parameters such as values for an A Rate, a VRate, an A Rate dispersion, a V Stability, an AV pattern, an NSRtemplate, an arrhythmia template, a detected or classifieddepolarization morphology, a measure of patient activity such as derivedfrom a Minute Ventilation (MV) sensor and/or an accelerometer, a measureof hemodynamic function such as derived from a hemodynamic sensor, anumber of past attempts required to treat a given observed condition, anidentification of a particular therapy that provided an effectivetreatment of an observed condition, an identification of a particulartherapy that provided an ineffective treatment of an observed condition,an age of a patient, an identification of medications known to be takenby the patient, etc and other environmental factors, including patient'sage, gender, and vital statistics at last observed exertion.

The cardiac therapy module 202 provides the medical therapies bycardioverting/defibrillating a heart. This module obtains its operatinginstructions from the cardiac neural network runtime module 204 toprovide either no therapy when no therapy is warranted or when noise isobserved, to provide one or more therapies from the collection ofmedical therapies as defined previously in an optimal or most efficientmanner depending upon the collection of observable parameters.

The cardiac device data collection module 203 collects values observedby the cardiac data interface module 201 immediately preceding andduring an event that gives rise to the cardiac therapy module 202providing therapies to a patent. These collected values are storedwithin the local data storage module 210 for later use. These values aretypically transmitted to the ANN processing system to update theoperating parameters associated with the therapies; however, suchcollected values may also be used by various cardiac device modulesduring the operation of the cardiac device 200. For example, thecollected values may include an indication of the last successfultherapy to correct a particular observed condition. This last successfultherapy may be utilized as a first attempted therapy if the particularobserved condition recurs at a later date and time.

The cardiac neural network runtime module 204 and ANN processing systeminterface module 205 provide a mechanism for determining a course oftreatment to deliver an optimal therapy to a patient for a given set ofobserved conditions. In the system according the present invention, arun-time only version of an artificial neural network is included withinthe cardiac device 200. The runtime version of the neural networkaccepts input data associated with observed conditions of a patient'sheart and generates a particular therapy or sequence of therapies to beutilized to correct the observed condition. The runtime module iscontrasted with the neural network training module found within the ANNprocessing system 300 that analyzes the data collected in the cardiacdevice data collection module 203 to determine a particular therapy orsequence of therapies to be utilized.

The artificial neural network disclosed herein corresponds to a genericneural network of no particular topology for the network of nodescontained therein. The neural network typically utilizes a form ofcompetitive learning for the operation of the nodes within the network.Within these learning networks, a large number of data vectors aredistributed in a high-dimensional space. These data vectors representknown values for experimental data that typically reflect a probabilitydistribution of the input observed data. From this probabilitydistribution representation, predictions for unknown values for similarinput data may be determined.

In all of these learning networks, the networks are typically presenteda set of input data that possesses a corresponding set of results data.From these data values, the network of nodes “learns” a relationshipbetween the input data and its corresponding results data. In thisprocess, the probability distribution relationship is estimated usingthe multi-dimensional network of nodes. This relationship is representedwithin a set of artificial neural network coefficients for a particulartopology of nodes.

One skilled in the art will recognize that competitive learning networksinclude a nearly infinite number of network topologies that may be usedto represent a particular probability distribution relationship withoutdeviating from the spirit and scope of the present invention as recitedwithin the attached claims. In addition, artificial neural networks mayutilize various well-known algorithm architectures, includinghard-competitive learning (i.e. “winner-take-all” learning), softcompetitive learning without a fixed network dimensionality, and softcompetitive learning with a fixed network dimensionality, to specify anartificial neural network according to the present invention as recitedwithin the attached claims. Each of these algorithm architecturesrepresents the same probability distribution relationship; however eachof the various algorithm architectures better optimize correspondingprocessing parameters which are often mutually exclusive with eachother. These parameters include error minimization or the minimizationof an expected quantization error, entropy maximization for thereference vectors used within a network, and topology-preserving orfeature mapping architectures that attempt to map high-dimensionalinputs signals onto lower-dimensional structures in a manner thatattempts to preserve similar relationships found within the originaldata within the post-mapping data. As such, any of these types ofalgorithm architectures may be used to construct an artificial neuralnetwork without deviating from the spirit and scope of the presentinvention as recited within the attached claims.

The ANN processing system interface module 205 provides the datacommunications functions between the cardiac device 200 and the ANNprocessing system 300 over the communications link 400. This moduletransfers the collected data from the data storage module 210 to the ANNprocessing system 300. This module 205 also accepts parameters used bythe cardiac neural network runtime module 204 from the ANN processingsystem 300 after updated parameters are generated.

FIG. 3A illustrates an example embodiment of an artificial neuralnetwork server module for use with a cardiac device module that is partof an overall system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to onepossible embodiment of the present invention. The artificial neuralnetwork processing system 300 comprises a training module 303, aprediction module 305, and a database of network node coefficients 310.The training module 303 is used with a set of collected data thatpossesses a corresponding set of observed parameter values obtained bythe cardiac device module 200 to generate a set of network nodecoefficients that represent a probability distribution relationship foran observed parameter data—observed results data set for a particularneural network topology and algorithm architecture. The training module303 includes a data learning input module that receives the observedparameter data—observed results data set generated using the learningprocess described above. The training module 303 also includes an ANNnode training module that processes the observed parameter data—observedresults data set to generate the coefficients used to specify theprobability distribution relationship and an ANN coefficient storagemodule 310 for storing the coefficients that have been previouslygenerated for later use.

The data processing within the training module 303 may proceed in abatch processing fashion in which all of the vectors within the observedparameter data—observed therapy data set are processed at a single time.In such a process, the observed parameter data—observed therapy data setis received by the interface module 301 from the cardiac device 200,processed by the training module 303, and the generated coefficients areplaced within the database 310 by the prediction module 305.Alternatively, the observed parameter data—observed therapy data set maybe processed as a sequence of smaller data sets in which the observedparameter data 315—observed therapy parameter 316 data set data valuesare generated at different times. In such a process, the training module303 uses the previously stored coefficients retrieved by the storagemodule along with a new small data set provided by the input module 312to generate an updated set of coefficients as shown in FIG. 3 b. Theseupdated coefficients may be once again stored within the database 310for use at a later time.

FIG. 3B illustrates an artificial neural network 331 implemented as aset of processing modules according to the present invention. Once anartificial neural network 331 has been trained as discussed above, aprediction module 321 may be used to predict, or classify, a particulartest data value 325. The prediction module 321 includes a dataprediction input module 322, an ANN prediction module 323, and an ANNoutput module 324. The data prediction input module 322 receives theinput test data generated as described above for use in the predictionmodule. The ANN prediction module 323 receives and utilizes the networkcoefficient values for the neural network from the ANN coefficientdatabase 332 to predict the possible result for the probabilitydistribution relationship specified within the neural network. Thisoutput value 326 is used by the ANN prediction module 305 to determineall possible values for a given observed therapy parameter, to determinean output set of therapy parameter value. This slope value is thenoutput for later use in ranking and classifying the individual therapiesused to determine preferred course of therapy for any given observedcondition.

The operation of the ANN processing system 300 typically involves anoperator connecting the ANN processing system 300 to the cardiac device200 and controlling the operation of the devices using a monitor 341 andkeyboard. The user interacts with a user interface module 304 toinstruct the devices to transfer collected data from the cardiac device,to input additional patient data, to train the neural network andgenerate an updated set of neural network node coefficients, and toupload the updated set of values to the cardiac device 200.

FIG. 4 illustrates a computing system that may be used to constructvarious computing systems that may be part of a distributed processingand communications system according to one embodiment of the presentinvention. In an exemplary embodiment of a ANN processing system 400,computing system 400 is operative to provide a neural network trainingand data collection system. Those of ordinary skill in the art willappreciate that the neural network training and data collection system400 may include many more components than those shown with reference toa computing system 300 shown in FIG. 3. However, the components shownare sufficient to disclose an illustrative embodiment for practicing thepresent invention. As shown in FIG. 3, neural network training and datacollection system 300 is connected to a cardiac device 200, or otherdevices as needed. Those of ordinary skill in the art will appreciatethat a network interface unit 410 includes the necessary circuitry forconnecting neural network training and data collection system 400 to anetwork of other computing systems, and is constructed for use withvarious communication protocols including the TCP/IP protocol.Typically, network interface unit 410 is a card contained within neuralnetwork training and data collection computing system.

ANN processing system 400 also includes processing unit 412, videodisplay adapter 414, and a mass memory, all connected via bus 422. Themass memory generally includes RAM 416, ROM 432, and one or morepermanent mass storage devices, such as hard disk drive 428, a tapedrive 438, CD-ROM/DVD-ROM drive 426, and/or a floppy disk drive. Themass memory stores operating system 420 for controlling the operation ofANN processing system 400. It will be appreciated that this componentmay comprise a general purpose server operating system as is known tothose of ordinary skill in the art, such as UNIX, MAC OS™, LINUX™, orMicrosoft WINDOWS NT®. Basic input/output system (“BIOS”) 418 is alsoprovided for controlling the low-level operation of processing system400.

The mass memory as described above illustrates another type ofcomputer-readable media, namely computer storage media. Computer storagemedia may include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules or other data. Examples of computer storage mediainclude RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by a computing device.

The mass memory also stores program code and data for providing asoftware development and neural network analysis and training system.More specifically, the mass memory stores applications including neuralnetwork based training program 430, programs 434, and similar analysistool applications 436. ANN processing program 400 includes computerexecutable instructions which are executed to perform the logicdescribed herein.

ANN processing system 400 also comprises input/output interface 424 forcommunicating with external devices, such as a mouse, keyboard, scanner,or other input devices not shown in FIG. 4. Likewise, ANN processingsystem 400 may further comprise additional mass storage facilities suchas CD-ROM/DVD-ROM drive 426 and hard disk drive 428. Hard disk drive 428is utilized by ANN processing system 400 to store, among other things,application programs, databases, and program data used by ANN processingsystem application program 430. The operation and implementation ofthese databases is well known to those skilled in the art.

FIG. 5 illustrates another example embodiment of a cardiac device moduleshowing data flow through a system for providing adaptive treatmenttherapies utilizing a neural network based learning engine according toanother example embodiment of the present invention. As discussed abovewith respect to FIG. 3, a cardiac data collection module 501 obtainsdata from electrical signals measured on a patient's heart 100. Thisdata may include any number of observable parameters including valuesfor an A Rate, a V Rate, an A Rate dispersion, a V Stability, an AVpattern, an NSR template, an arrhythmia template, a detected orclassified depolarization morphology, a measure of patient activity suchas derived from a Minute Ventilation (MV) sensor and/or anaccelerometer, a measure of hemodynamic function such as derived from ahemodynamic sensor, a number of past attempts required to treat a givenobserved condition, an identification of a particular therapy thatprovided an effective treatment of an observed condition, anidentification of a particular therapy that provided an ineffectivetreatment of an observed condition, an age of a patient, anidentification of medications known to be taken by the patient, etc.This collected data may be passed to a cardiac data storage module 510for storage into a local data store 511 for later use after beingtransmitted to the ANN processing system 300 by a ANN system interfacemodule 512.

This data may also be passed to a cardiac data transformation module 502that combines one or more of the above collected data values into a setof one or more health indications for use by the artificial neuralnetwork runtime module 503. For example, the collected data values maybe combined to generate a health state of the patient's heart 100 thatpossesses various values such as good, average and bad, poor or similarstate classifications. These classifications may then be used by the ANNruntime module 503 to generate an appropriate therapy, if any, is to beapplied to the patent's heart.

Of course, one skilled in the art will recognize that the transformationmodule 502 may be eliminated in its entirety and the collected data usedby the ANN runtime module 503 without deviating from the spirit andscope of the present invention as recited within the attached claims.The elimination of the transformation module 502 may increase thecomplexity of the neural network used within the cardiac device as welland the processing needed to utilize such a network. However, the use ofthe transformation module 502 reduces the amount of data available tothe ANN runtime module, and as such may reduce the accuracy of theprediction function associated with selecting an optimal therapy.

The ANN runtime module 503 uses the data received from thetransformation module 502 to generate commands to a therapy module 504that provides the course of therapies provided to a patient. Asdiscussed above, these may include pacing, ATP, low energy therapy andmax energy therapy depending upon the observed data.

FIG. 6 illustrates another example embodiment of an artificial neuralnetwork server module for use with a cardiac device module showing dataflow through a system for providing adaptive treatment therapiesutilizing a neural network based learning engine according to anotherpossible embodiment of the present invention. The ANN processing systemreceives collected patient data from the cardiac device 200 through acardiac device interface module 601 for use in training an artificialneural network resident both in the cardiac device as well as the ANNprocessing system 300. Typically, this operation occurs in a medicalfacility on a periodic basis as a patient seeks to optimize theoperation of the cardiac device module 200. The frequency of theseupdate operations may vary from patient to patient depending upon anumber of factors including the nature of the cardiac problems of theparticular patient, any changes in the nature of the cardiac problems ofthe particular patient, and the effectiveness of the adaptive treatmenttherapies provided by the cardiac device module for the particularpatient.

The collected patient data is passed to an ANN patient data processingmodule 602 which performs preprocessing and data transformation similarto processing that occurs in the cardiac device module 200. Once thedata has been preprocessed as needed, the data is passed to anartificial neural network module 604 to update and train the neuralnetwork. This neural network module 604 includes any training andvalidation modules needed to calculate the coefficients associated withthe nodes within the neural network. The form and operation of theneural network module exactly matches the nodes in the runtime neuralnetwork located within the cardiac device module 200. The artificialneural network module processes the collected patient data along withother data received from a therapy data store 610 using a patient datamodule 605 that defined the course of therapy used within the cardiacdevice 200. The patient data module 605 may also receive data from anoperator of the ANN processing system 300 for use in training the neuralnetwork in the neural network module 604.

The coefficients associated with the nodes of the neural network arepassed to an ANN processing network update module 603. This module 603formats and passes the data updates to the cardiac device module 200 foruse in providing adaptive therapies to patients. The update module 603also interacts with the cardiac device interface module 601 to performthe data communications with the cardiac device module 200 over thecommunications link 400.

The sequence of possible therapies to be applied by the cardiac devicemodule 200 may include any number of possible treatment options that areapplied using a sequence of criteria. The rules and control over whenany of the possible treatment options are applied to a patient isembedded within the operation of the artificial neural network. Theruntime module in the cardiac device implements these possible treatmentoptions through the application of the artificial neural network topatient data collected by the cardiac device module 200. One skilled inthe art will recognize that a number of possible variations on thetreatment options as applied to a given patient may arise utilizing theartificial neural network as recited herein within the attached claimswithout deviating from the spirit and scope of the present invention.

FIG. 4 illustrates an example of a suitable operating environment inwhich the invention may be implemented. The operating environment isonly one example of a suitable operating environment and is not intendedto suggest any limitation as to the scope of use or functionality of theinvention. Other well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

The invention may also be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more computers or other devices. Generally, program modulesinclude routines, programs, objects, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. Typically the functionality of the program modules may becombined or distributed as desired in various embodiments.

Processing devices attached to a communications network typicallyincludes at least some form of computer readable media. Computerreadable media can be any available media that can be accessed by thesedevices. By way of example, and not limitation, computer readable mediamay comprise computer storage media and communication media. Computerstorage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other medium which can be used tostore the desired information and which can be accessed by processingdevices.

Communication media typically embodies computer readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer readablemedia.

Additionally, the embodiments described herein are implemented aslogical operations performed by a programmable processing devices. Thelogical operations of these various embodiments of the present inventionare implemented (1) as a sequence of computer implemented steps orprogram modules running on a computing system and/or (2) asinterconnected machine modules or hardware logic within the computingsystem. The implementation is a matter of choice dependent on theperformance requirements of the computing system implementing theinvention. Accordingly, the logical operations making up the embodimentsof the invention described herein can be variously referred to asoperations, steps, or modules.

While the above embodiments of the present invention describe adistributed processing for providing adaptive medical therapiesutilizing a neural network based learning engine, one skilled in the artwill recognize that the use of a particular computing architecture for adisplay computing system and a web server are merely example embodimentsof the present invention. It is to be understood that other embodimentsmay be utilized and operational changes may be made without departingfrom the scope of the present invention as recited in the attachedclaims.

As such, the foregoing description of the exemplary embodiments of theinvention has been presented for the purposes of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many modifications andvariations are possible in light of the above teaching. It is intendedthat the scope of the invention be limited not with this detaileddescription, but rather by the claims appended hereto. The presentinvention is presently embodied as a method, apparatus, and article ofmanufacture for providing an ANN processing system 400 for providingadaptive medical therapies utilizing a neural network based learningengine.

1. A system, comprising: an artificial neural network comprising a first portion and a second portion; an implantable cardiac device including: means for applying corrective medical therapies to a patient; means for collecting at least two different types of health-related data values from the patient; means for combining the at least two different types of health-related data values into a set of one or more health indications; and the first portion of the artificial neural network, the first portion adapted to provide real time network operation and to adapt and determine applied corrective medical therapies based on the set of health indications; and an external data processing apparatus including: the second portion of the artificial neural network, the second portion adapted to train and validate the operation of the neural network and to determine a set of operating coefficients based on the collected data values, wherein the cardiac device and data processing apparatus are adapted to communicate to pass the collected patient data values from the device to the apparatus, and are further adapted to communicate to pass the set of operating coefficients from the apparatus to the device, the coefficients used by the artificial neural network when determining the applied corrective medical therapies.
 2. The system of claim 1, wherein the external data processing apparatus further includes: means for storing the collected patient data values, the storing means adapted to maintain all patient collected data history and treatment therapies.
 3. The system of claim 1, wherein the means for collecting health-related data values is adapted to collect data values associated with a number of past attempts required to treat a given observed condition.
 4. The system of claim 1, wherein the means for collecting health-related data values is adapted to collect data values associated with an identity of medications known to be taken by the patient.
 5. The system of claim 1, wherein the means for collecting health-related data values is adapted to collect data values associated with an age of the patient.
 6. The system of claim 1, wherein the means for collecting health-related data values is adapted to collect data values associated with vital statistics at a last observed exertion of the patient.
 7. The system of claim 1, wherein the first portion of the artificial neural network and the second portion of the artificial neural network implement identical networks of nodes.
 8. The system of claim 1, wherein the means for collecting health-related data values is adapted to collect data values associated with a measure of patient activity.
 9. The system of claim 8, wherein data values associated with a measure of patient activity include data values derived from a minute ventilation (MV) sensor.
 10. The system of claim 8, wherein data values associated with a measure of patient activity include data values derived from an accelerometer.
 11. A method, comprising: forming an artificial neural network comprising a first portion and a second portion; forming an implantable cardiac device including: forming means for applying corrective medical therapies to a patient; forming means for collecting at least two different types of health-related data values from the patient; forming means for combining the at least two different types of health-related data values into a set of one or more health indications; and forming the first portion of an artificial neural network, the first portion adapted to provide real time network operation and to adapt and determine applied corrective medical therapies based on the set of health indications; and forming an external data processing apparatus including: forming the second portion of the artificial neural network, the second portion adapted to train and validate the operation of the neural network and to determine a set of operating coefficients based on the collected data values, wherein the cardiac device and data processing apparatus are formed to communicate to pass the collected patient data values from the device to the apparatus, and are further formed to communicate to pass the set of operating coefficients from the apparatus to the device, the coefficients used by the artificial neural network when determining the applied corrective medical therapies.
 12. The method of claim 11, wherein the means for collecting two or more health-related data values includes means for collecting data values associated with the current state of the patient's heart.
 13. The method of claim 12, wherein the means for collecting data values associated with the current state of the patient's heart includes means for receiving an NSR template.
 14. The method of claim 12, wherein the means for collecting data values associated with the current state of the patient's heart includes means for receiving an AV pattern.
 15. The method of claim 12, wherein the means for collecting data values associated with the current state of the patient's heart includes means for receiving an arrhythmia template.
 16. The method of claim 11, wherein the means for collecting two or more health-related data values includes means for collecting data regarding an identity of a particular therapy that provided an effective treatment of an observed condition.
 17. The method of claim 11, wherein the means for collecting two or more health-related data values includes means for collecting data regarding an identity of a particular therapy that provided an ineffective treatment of an observed condition.
 18. The method of claim 11, wherein the means for collecting two or more health-related data values includes means for collecting data regarding a number of past attempts required to treat a given observed condition.
 19. The method of claim 11, further comprising: forming a means for storing the collected data values.
 20. The method of claim 19, wherein forming a means for storing the collected data values includes forming a database. 